The present invention relates to encoding an event time series using a dictionary, and more specifically, to discovering temporal patterns across multiple time series while taking into account event inter-arrival times.
Conventionally, methods for encoding multiple events arriving from compact temporal patterns or arriving from ordered stream of vectors create a dictionary with symbols for representing temporal pattern of events in a compact and understandable manner. The conventional methods merely take into account the sequential order of events.
However, the aforementioned conventional methods do not consider temporal aspects of the data, and especially do not transform inter-arrival time between events into discrete symbols. For example, the conventional methods do not calculate the time between events and the conventional methods only focus on the sequential order of the events.
Other conventional methods to encoding an event time series provide a compact model representing pattern and duration of events such as conventional data mining.
However, these other conventional methods deal with creating a statistical model (mixture model) from various event sequences, while they fail to consider encoding a given event sequence. The present inventors have recognized that the problem with conventional data mining is that the temporal dimension between the sequences of events is not considered. For example, the conventional methods only map the sequence of the events without taking into consideration the inter-arrival times between the events.